Microsoft AI-103 : Developing AI Apps and Agents on Azure

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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure image moderation to block unsafe content before processing the images.
Does this meet the goal?

A) No
B) Yes

2. You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?

A) the custom speech project ID
B) the project URL
C) the custom speech endpoint URL
D) the project ID

3. You need to recommend an invoice review solution that resolves the issue reported by the finance department.
What should you include in the recommendation?

A) chat completions
B) Azure Document Intelligence in Foundry Tools
C) Image Analysis
D) Azure Content Understanding in Foundry Tools

4. You have a Microsoft Foundry project that contains a customer support agent grounded in internal documentation.
After a recent update, users report the following issues:
* Some answers are unsupported by retrieved documents.
* A small number of responses are flagged for policy violations.
You need to evaluate each issue.
Which observability signals should you use for each issue? To answer, drag the appropriate observability signals to the correct issues. Each observability signal may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

5. You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed line items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
* Extracts the invoice number, invoice date, vendor name, and total amount across varying templates
* Returns confidence scores so that results with confidence below 0.80 can be routed for supervisor review What should you use?

A) the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
B) a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores
C) a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing
D) the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing

Solutions: